A Multi-Vehicle Cooperative Localization Method Based on Belief Propagation in Satellite Denied Environment

被引：0

作者：

Wang J. ^{[1
]}

Wang L. ^{[1
]}

机构：

[1] School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing

来源：

Journal of Beijing Institute of Technology (English Edition) | 2022年 / 31卷 / 05期

基金：

中国国家自然科学基金;

关键词：

belief propagation; cooperative localization; factor graph; inertial navigation system; internet of vehicles;

D O I：

10.15918/j.jbit1004-0579.2022.029

中图分类号：

学科分类号：

摘要：

The global navigation satellite system (GNSS) is currently being used extensively in the navigation system of vehicles. However, the GNSS signal will be faded or blocked in complex road environments, which will lead to a decrease in positioning accuracy. Owing to the higher-precision synchronization provided in the sixth generation (6G) network, the errors of ranging-based positioning technologies can be effectively reduced. At the same time, the use of terahertz in 6G allows excellent resolution of range and angle, which offers unique opportunities for multi-vehicle cooperative localization in a GNSS denied environment. This paper introduces a multi-vehicle cooperative localization method. In the proposed method, the location estimations of vehicles are derived by utilizing inertial measurement and then corrected by exchanging the beliefs with adjacent vehicles and roadside units. The multi-vehicle cooperative localization problem is represented using a factor graph. An iterative algorithm based on belief propagation is applied to perform the inference over the factor graph. The results demonstrate that our proposed method can offer a considerable capability enhancement on localization accuracy. © 2022 Beijing Institute of Technology. All rights reserved.

引用

页码：464 / 472

页数：8

共 26 条

[1] Liu Y., Xin M., Wu Y., Liu H., Wang X., Yang F., Global positioning performance analysis of BeiDou satellite navigation system, Fire Control & Command Control, 45, 8, pp. 131-135, (2020)
[2] Vu T. D., Aycard O., Tango F., Object perception for intelligent vehicle applications: A multi-sensor fusion approach, 2014 IEEE Intelligent Vehicles Symposium Proceedings, pp. 774-780, (2014)
[3] Gu S., Analysis and mitigation of NLOS errors in GNSS applications in urban canyons, (2021)
[4] Feng J., Yang X., Ma H., Wang J., Antijamming algorithm based on spatial blind search for global navigation satellite system receiver, Journal of Beijing Institute of Technology, 29, 1, pp. 103-109, (2020)
[5] Situ C., Precise positioning of intelligent vehicles based on integrated navigation system, Electronic Test, 27, 1, pp. 99-101, (2020)
[6] Lim J. H., Choi K. H., Kim L. W., Lee H. K., Land vehicle positioning in urban area by integrated GPS/BeiDou/OBD-II/MEMS IMU, IEEE International Conference on Intelligent Transportation Engineering, (2016)
[7] Xu Z., Research on vehicle integrated navigation technology based on MEMS IMU, (2017)
[8] Liu Y., Fan X., Chen L., Jian W., Liang L., Ding D., An innovative information fusion method with adaptive Kalman filter for integrated INS/GPS navigation of autonomous vehicles, Mechanical Systems and Signal Processing, 100, pp. 605-616, (2018)
[9] Li X., Chen W., Chan C., Li B., Song X., Multi-sensor fusion methodology for enhanced land vehicle positioning, Information Fusion, 46, pp. 51-62, (2019)
[10] Dai Q., Sui L., Wang L., Tian Z., Yuan T., An efficiency algorithm on Gaussian mixture UKF for BDS/INS navigation system, Geodesy and Geodynamics, 9, 2, pp. 169-174, (2018)

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